1 Source libraries, functions and plotting parameters

library(here)

source(here::here("code/scripts/20210628_source.R"))

2 Get full list of traits, associations and studies from the GWAS Catalog

2.1 Traits

NOTE: only run once.

date_of_collection = "20210809"
long_term_dir = "/nfs/research/birney/users/ian/hmn_fst/gwasrapidd"
out_file = here::here("code/snakemake/20210625/config",
                      paste(date_of_collection, "_all_traits.tsv", sep = ""))

# Get list of all traits in database
all_traits = gwasrapidd::get_traits()

# Filter out IDs that cause an error
all_traits_tbl = all_traits@traits %>% 
  dplyr::filter(efo_id != "CHEBI:7916") %>%
  # Save to file
  readr::write_tsv(out_file)
all_traits_tbl %>% 
  DT::datatable(.)

2.2 Associations

NOTE: only run once.

#Need to do them sequentially because the GWAS Catalog can't handle dozens (let alone thousands) of simultaneous queries.
date_of_collection = "20210809"
long_term_dir = "/nfs/research/birney/users/ian/hmn_fst/gwasrapidd"

# create output dir
out_dir = file.path(long_term_dir,
                    date_of_collection,
                    "associations_raw")
dir.create(out_dir, recursive = T)

# Get associations
counter = 0
lapply(all_traits_tbl$efo_id, function(EFO_ID) {
  counter <<- counter + 1
  # set output file name
  out_path = file.path(out_dir,
                       paste(EFO_ID, ".rds", sep = ""))
  # if output file doesn't already exist, get associations and save
  if (!file.exists(out_path)){
    out = gwasrapidd::get_associations(efo_id = EFO_ID)
    saveRDS(out, out_path)
  } else {
    print(paste(counter, ". ", EFO_ID, ": File already exists.", sep = ""))
  }
})

3 Run Snakemake pipeline to extract data

Full Snakemake pipeline here: https://github.com/brettellebi/human_traits_fst/tree/master/code/snakemake/20210625

4 Read in processed data

4.1 Specify target directory and clumping parameters

target_dir = "/nfs/research/birney/users/ian/hmn_fst/gwasrapidd/20210809"
clump_params = "0.1_1000"

4.2 Total initial counts

assocs_path = file.path(target_dir, "associations_raw")

assocs_files = list.files(assocs_path, full.names = T)

# Read in associations
assocs_raw = lapply(assocs_files, function(EFO_ID){
  readRDS(EFO_ID)
})

names(assocs_raw) = basename(assocs_files) %>% 
  stringr::str_remove(".rds")

# Get counts
raw_counts = purrr::map(assocs_raw, function(EFO_ID){
  EFO_ID@risk_alleles %>%
    dplyr::pull(variant_id) %>% 
    unique(.) %>% 
    length(.) %>% 
    data.frame("N" = .)
}) %>% 
  dplyr::bind_rows(.id = "EFO_ID") %>% 
  # Add `trait`
  dplyr::left_join(all_traits_tbl %>% 
                     dplyr::select(efo_id, trait),
                   by = c("EFO_ID" = "efo_id")) %>% 
  # Rename to `TRAIT`
  dplyr::rename(TRAIT = trait) %>% 
  # Remove all traits with N < 1
  dplyr::filter(N >= 1)

raw_counts %>% 
  dplyr::arrange(dplyr::desc(N)) %>% 
  DT::datatable(.)

4.3 Hits

Set minimum number of “independent” (i.e. post-clumped) SNPs per trait

min_snps = 10
hits_path = file.path(target_dir, "pegas/fst/consol", clump_params, "hits.rds")
hits = readRDS(hits_path) %>%
  lapply(., function(TRAIT_ID){
    # convert to DF
    TRAIT_ID %>%
        data.frame(.) %>% 
        tibble::rownames_to_column(var = "SNP") %>% 
        dplyr::select(SNP, FST_PEGAS = Fst)
  }) %>% 
  dplyr::bind_rows(.id = "EFO_ID") %>% 
  dplyr::left_join(all_traits_tbl %>% 
                     dplyr::select(EFO_ID = efo_id,
                                   TRAIT = trait),
                   by = "EFO_ID")

# Get counts
hits_counts = hits %>% 
  dplyr::count(EFO_ID, TRAIT) %>% 
  dplyr::rename(N = n)

hits_counts %>% 
  dplyr::arrange(dplyr::desc(N)) %>% 
  DT::datatable(.)
# Get vector of EFO_IDs with more than `min_snps`
keep_snps = hits %>%
  dplyr::count(EFO_ID) %>%
  dplyr::filter(n >= min_snps) %>% 
  dplyr::pull(EFO_ID)

# How many traits pass threshold
length(keep_snps)
## [1] 587
# Filter hits for those traits
hits_filt = hits %>% 
  dplyr::filter(EFO_ID %in% keep_snps)

4.4 Controls

NOTE one SNP ID can map to multiple loci, so there are duplicate SNP IDs with different Fst measures.

In the code below we remove all the duplicated SNPs which have periods in their names, e.g. {SNP_ID}.1 or {SNP_ID}.2.

That is, we keep the first SNP in the genome with the ID. This would bias toward SNPs “earlier” in the genome. Probably not consequential?

# Fst controls
controls_path = file.path(target_dir, "pegas/fst/consol", clump_params, "controls.rds")
controls = readRDS(controls_path) %>%
  lapply(., function(TRAIT_ID){
    # convert to DF
    TRAIT_ID %>%
        data.frame(.) %>% 
        tibble::rownames_to_column(var = "SNP") %>% 
        dplyr::select(SNP, FST_PEGAS = Fst)
  }) %>% 
  dplyr::bind_rows(.id = "EFO_ID") %>% 
  dplyr::left_join(all_traits_tbl %>% 
                     dplyr::select(EFO_ID = efo_id,
                                   TRAIT = trait),
                   by = "EFO_ID") %>% 
  # NOTE: remove duplicated IDs
  dplyr::filter(!grepl("\\.", SNP))

# Filter controls for traits with more than `min_snps` post-clumping
controls_filt = controls %>% 
  dplyr::filter(EFO_ID %in% keep_snps)

4.6 Add palette based on pre-clump SNP count

pal_all = turbo(n = nrow(raw_counts))
names(pal_all) = raw_counts %>% 
  dplyr::arrange(desc(N)) %>% 
  dplyr::pull(TRAIT)

5 SNP counts pre- and post-clumping

snp_count_plot = list("PRE-CLUMP" = raw_counts,
                      "POST-CLUMP" = hits_counts) %>% 
  dplyr::bind_rows(.id = "FILTER") %>% 
  # order variables
  dplyr::mutate(TRAIT = factor(TRAIT, levels = names(pal_all)),
                FILTER = factor(FILTER, levels = c("PRE-CLUMP", "POST-CLUMP"))) %>% 
  ggplot() +
    geom_col(aes(TRAIT, N, fill = TRAIT)) +
    scale_fill_manual(values = pal_all) +
    theme_bw() +
    theme(axis.text.x = element_blank(),
          axis.title.x = element_blank(),
          axis.ticks.x = element_blank()) +
    ylab("N LOCI") +
    facet_wrap(~FILTER, nrow = 2) +
    guides(fill = "none")

plotly::ggplotly(snp_count_plot,
                 tooltip = c("TRAIT"))
width = 20
height = 10

dir.create(here::here("docs/plots", clump_params), recursive = T, showWarnings = T)

ggsave(here::here("docs/plots", clump_params, "20220314_snp_counts.png"),
       plot = snp_count_plot,
       device = "png",
       width = width,
       height = height,
       units = "in",
       dpi= 400)

ggsave(here::here("docs/plots", clump_params, "20220314_snp_counts.svg"),
       plot = snp_count_plot,
       device = "svg",
       width = width,
       height = height,
       units = "in")

6 Rank by KS divergence

Ranking by median Fst doesn’t capture the difference between pigmentation/HIV traits, and all others.

This difference is captured by the D statistic, but we would want a direction – i.e. do the hits have higher or lower \(F_{ST}\) than the controls?

Then we want to rank by that directional difference.

6.1 Create DF

# Create full data frame of hits and controls
hits_v_controls_full = list("HIT" = hits_filt,
                            "CONTROL" = controls_filt) %>% 
  dplyr::bind_rows(.id = "TYPE") %>% 
  # create column for palette
  dplyr::mutate(PAL_TRAIT = dplyr::if_else(TYPE == "HIT",
                                           TRAIT,
                                           "control"))

6.2 Rank by divergence from controls

# Run KS.test
# Split into list by trait
ks_list_full = hits_v_controls_full %>% 
  split(., f = .$TRAIT) %>% 
  purrr::map(., function(TRAIT){
    split(TRAIT, f = TRAIT$TYPE)
  })

# `Run ks.test`
ks_out_full = purrr::map(ks_list_full, function(TRAIT){
  # Create output list
  out = list()
  
  # Run KS tests
  out[["two.sided"]] = ks.test(TRAIT$HIT$FST_PEGAS,
                               TRAIT$CONTROL$FST_PEGAS,
                               alternative = "two.sided")
  out[["less"]] = ks.test(TRAIT$HIT$FST_PEGAS,
                        TRAIT$CONTROL$FST_PEGAS,
                        alternative = "less")
  out[["greater"]] = ks.test(TRAIT$HIT$FST_PEGAS,
                             TRAIT$CONTROL$FST_PEGAS,
                             alternative = "greater")

  return(out)
})

# Pull out stats
ks_stats_full = purrr::map_dfr(ks_out_full, function(TRAIT){
  tibble::tibble(D_TWO = TRAIT$two.sided$statistic,
                 D_LESS = TRAIT$less$statistic,
                 D_GREATER = TRAIT$greater$statistic)
}, .id = "TRAIT")

The alternative = "two.sided" result is just the highest D of either “less” or “greater”.

ks_stats_full %>%
  dplyr::arrange(desc(D_TWO)) %>% 
  DT::datatable(.)

The documentation for ks.test says: >… in the two-sample case alternative = “greater” includes distributions for which x is stochastically smaller than y (the CDF of x lies above and hence to the left of that for y)…

So for traits with hits that have a lower \(F_{ST}\) than their controls will have a higher D under the "greater" test than the "less" test, e.g. body mass index.

Give those a negative sign:

ks_stats_full = ks_stats_full %>% 
  dplyr::mutate(D_SIGNED = dplyr::case_when(D_GREATER > D_LESS ~ -D_TWO,
                                            D_GREATER < D_LESS ~ D_TWO,
                                            D_GREATER == D_LESS ~ 0)) %>% 
  dplyr::arrange(D_SIGNED)

ks_stats_full %>% 
  DT::datatable(.)

6.3 Create palette with this ranking

traits_d_ranked = ks_stats_full$TRAIT

d_rank_pal = viridisLite::turbo(n = length(traits_d_ranked))
names(d_rank_pal) = traits_d_ranked

6.4 Plot faceted eCDFs of all traits ranked by difference from controls

ecdf_plot_face_w_controls_d_rank = hits_v_controls_full %>%
  # take unique SNPs
  dplyr::group_by(EFO_ID) %>%
  dplyr::distinct(SNP, .keep_all = T) %>% 
  dplyr::ungroup() %>% 
  # order FST_PEGAS so that it's properly plotted by plotly
  dplyr::arrange(EFO_ID, FST_PEGAS) %>% 
  # order
  dplyr::mutate(TRAIT = factor(TRAIT, levels = names(d_rank_pal)),
                PAL_TRAIT = factor(PAL_TRAIT, levels = names(d_rank_pal))) %>% 
  ggplot() +
    stat_ecdf(aes(FST_PEGAS, colour = PAL_TRAIT)) +
    scale_colour_manual(values = d_rank_pal) +
    theme_cowplot(rel_small = 9/14) +
    guides(colour = "none") +
    theme(strip.text.x = element_text(size = 5)) +
    xlab(expression(italic(F[ST]))) +
    ylab("Cumulative Probability") +
    facet_wrap(~TRAIT) 

out_path = here::here("docs/plots", clump_params, "20220314_ecdf_all_faceted_with_controls_d_rank.png")
dir.create(dirname(out_path), recursive = T, showWarnings = F)

ggsave(out_path,
       plot = ecdf_plot_face_w_controls_d_rank, 
       device = "png",
       width = 30,
       height = 22,
       units = "in",
       dpi = 400)
## Warning: Removed 154 rows containing non-finite values (stat_ecdf).
knitr::include_graphics(out_path)

6.5 Plot eCDFs of all traits ranked by difference from controls

plotly_ecdf_all_d_ranked = hits_filt %>% 
  # order FST_PEGAS so that it's properly plotted by plotly
  dplyr::arrange(EFO_ID, FST_PEGAS) %>% 
  # order
  dplyr::mutate(TRAIT = factor(TRAIT, levels = names(d_rank_pal))) %>% 
  ggplot() +
    stat_ecdf(aes(FST_PEGAS, colour = TRAIT)) +
    scale_colour_manual(values = d_rank_pal) +
    theme_bw() +
    ggtitle("eCDF") +
    guides(colour = "none") +
    theme(strip.text.x = element_text(size = 5))

plotly::ggplotly(plotly_ecdf_all_d_ranked,
                 tooltip = c("TRAIT"))
## Warning: Removed 5 rows containing non-finite values (stat_ecdf).

6.6 Plot eCDFs of selected traits

new_traits = c(
  "heart rate variability measurement",
  "alcohol abuse",
  "aging",
  "blood pressure",
  "BMI-adjusted waist circumference",
  "body mass index",
  "intelligence",
  "self reported educational attainment",
  "asthma",
  "body height",
  "mathematical ability",
  "type ii diabetes mellitus",
  "hair colour measurement",
  "cognitive function measurement",
  "schizophrenia",
  "brain measurement",
  "Alzheimer's disease",
  "stroke",
  "hair color",
  "hypertension",
  "Myopia",
  "BMI-adjusted hip circumference",
  "hair shape measurement",
  "eye colour measurement",
  "skin pigmentation",
  "skin pigmentation measurement",
  "HIV-1 infection",
  "viral load"
)

6.7 Final plots

6.7.1 Ridges

ridges_plot_d_rank = hits_filt %>% 
  # filter for `target_traits`
  dplyr::filter(TRAIT %in% new_traits) %>% 
  # group by trait to take unique SNPs
  dplyr::group_by(TRAIT) %>% 
  dplyr::distinct(SNP, .keep_all = T) %>% 
  dplyr::ungroup() %>% 
  # reverse order of traits to put `body height` at the top
  dplyr::mutate(TRAIT_REV = factor(TRAIT, levels = rev(names(d_rank_pal)))) %>% 
  # plot
  ggplot(aes(FST_PEGAS, TRAIT_REV, fill = TRAIT_REV, colour = TRAIT_REV)) +
    geom_density_ridges(scale = 2,
                        bandwidth = 0.003,
                        calc_ecdf = TRUE,
                        quantiles = 0.5,
                        quantile_lines = T,
                        jittered_points = T,
                        point_shape = '|', alpha = 0.85, point_size = 2,
                        position = position_points_jitter(height = 0),
                        ) +
    scale_fill_manual(values = d_rank_pal) +
    scale_colour_manual(values = darker(d_rank_pal)) +
    guides(fill = "none", colour = "none") +
    theme_cowplot() +
    scale_y_discrete(expand = expansion(add = c(0.2, 2.3))) +
    xlab(expression(italic(F[ST]))) +
    ylab(NULL)

ridges_plot_d_rank

6.7.2 Faceted

ecdf_plot_face_d_rank = hits_v_controls_full %>%
  # filter for `target_traits`
  dplyr::filter(TRAIT %in% new_traits) %>%
  # take unique SNPs
  dplyr::group_by(EFO_ID) %>%
  dplyr::distinct(SNP, .keep_all = T) %>% 
  dplyr::ungroup() %>% 
  # order FST_PEGAS so that it's properly plotted by plotly
  dplyr::arrange(EFO_ID, FST_PEGAS) %>% 
  # order
  dplyr::mutate(TRAIT = factor(TRAIT, levels = names(d_rank_pal)),
                PAL_TRAIT = factor(PAL_TRAIT, levels = names(d_rank_pal))) %>% 
  ggplot() +
    stat_ecdf(aes(FST_PEGAS, colour = PAL_TRAIT)) +
    scale_colour_manual(values = d_rank_pal) +
    theme_cowplot(rel_small = 9/14) +
    guides(colour = "none") +
    theme(strip.text.x = element_text(size = 5)) +
    xlab(expression(italic(F[ST]))) +
    ylab("Cumulative Probability") +
    facet_wrap(~TRAIT, nrow = 7, ncol = 4) 

ecdf_plot_face_d_rank
## Warning: Removed 20 rows containing non-finite values (stat_ecdf).

ggsave(here::here("docs/plots", clump_params, "20220314_ecdf_all_faceted_with_controls_d_rank.png"),
       plot = ecdf_plot_face_d_rank, 
       device = "png",
       width = 8,
       height = 10,
       units = "in",
       dpi = 400)
## Warning: Removed 20 rows containing non-finite values (stat_ecdf).

6.7.3 Together

ecdf_plot_all_d_rank = hits_filt %>%
  # filter for `target_traits`
  dplyr::filter(TRAIT %in% new_traits) %>%
  # take unique SNPs
  dplyr::group_by(EFO_ID) %>%
  dplyr::distinct(SNP, .keep_all = T) %>% 
  dplyr::ungroup() %>% 
  # order FST_PEGAS so that it's properly plotted by plotly
  dplyr::arrange(EFO_ID, FST_PEGAS) %>% 
  # order
  dplyr::mutate(TRAIT = factor(TRAIT, levels = names(d_rank_pal))) %>% 
  ggplot() +
    stat_ecdf(aes(FST_PEGAS, colour = TRAIT)) +
    scale_colour_manual(values = d_rank_pal) +
    theme_cowplot() +
    guides(colour = "none") +
    theme(strip.text.x = element_text(size = 5)) +
    xlab(expression(italic(F[ST]))) +
    ylab("Cumulative Probability")

ecdf_plot_all_d_rank

6.7.4 Final arranged

final_figure = cowplot::ggdraw() +
  draw_plot(ridges_plot_d_rank,
            x = 0, y = 0, width = .5, height = 1) +
  draw_plot(ecdf_plot_face_d_rank,
            x = .5, y = 0.35, width = .5, height = .65) +
  draw_plot(ecdf_plot_all_d_rank,
            x = .5, y = 0, width = .5, height = .35) +
  draw_plot_label(label = c("A", "B", "C"), size = 25,
                  x = c(0, .5, .5), y = c(1, 1, .35),
                  hjust = c(-.15, .15, .15),
                  color = "black")
## Warning: Removed 20 rows containing non-finite values (stat_ecdf).
width = 12
height = 12

ggsave(here::here("docs/plots", clump_params, "20220314_final.png"),
       final_figure,
       device = "png",
       width = width,
       height = height,
       units = "in",
       dpi= 400)

ggsave(here::here("docs/plots", clump_params, "20220314_final.pdf"),
       final_figure,
       device = "pdf",
       width = width,
       height = height,
       units = "in",
       dpi= 400)
knitr::include_graphics(here::here("docs/plots", clump_params, "20220314_final.png"))